Radix-2 x 2 x 2 algorithm for the 3-D discrete hartley transform

The discrete Hartley transform (DHT) has proved to be a valuable tool in digital signal/image processing and communications and has also attracted research interests in many multidimensional applications. Although many fast algorithms have been developed for the calculation of one- and two-dimensional (1-D and 2-D) DHT, the development of multidimensional algorithms in three and more dimensions is still unexplored and has not been given similar attention; hence, the multidimensional Hartley transform is usually calculated through the row-column approach. However, proper multidimensional algorithms can be more efficient than the row-column method and need to be developed. Therefore, it is the aim of this paper to introduce the concept and derivation of the three-dimensional (3-D) radix-2 2X 2X algorithm for fast calculation of the 3-D discrete Hartley transform. The proposed algorithm is based on the principles of the divide-and-conquer approach applied directly in 3-D. It has a simple butterfly structure and has been found to offer significant savings in arithmetic operations compared with the row-column approach based on similar algorithms

Review of the mathematical foundations of data fusion techniques in surface metrology

The recent proliferation of engineered surfaces, including freeform and structured surfaces, is challenging current metrology techniques. Measurement using multiple sensors has been proposed to achieve enhanced benefits, mainly in terms of spatial frequency bandwidth, which a single sensor cannot provide. When using data from different sensors, a process of data fusion is required and there is much active research in this area. In this paper, current data fusion methods and applications are reviewed, with a focus on the mathematical foundations of the subject. Common research questions in the fusion of surface metrology data are raised and potential fusion algorithms are discussed

Three dimensional pattern recognition using feature-based indexing and rule-based search

In flexible automated manufacturing, robots can perform routine operations as well as recover from atypical events, provided that process-relevant information is available to the robot controller. Real time vision is among the most versatile sensing tools, yet the reliability of machine-based scene interpretation can be questionable. The effort described here is focused on the development of machine-based vision methods to support autonomous nuclear fuel manufacturing operations in hot cells; This thesis presents a method to efficiently recognize 3D objects from 2D images based on feature-based indexing. Object recognition is the identification of correspondences between parts of a current scene and stored views of known objects, using chains of segments or indexing vectors. To create indexed object models, characteristic model image features are extracted during preprocessing. Feature vectors representing model object contours are acquired from several points of view around each object and stored. Recognition is the process of matching stored views with features or patterns detected in a test scene; Two sets of algorithms were developed, one for preprocessing and indexed database creation, and one for pattern searching and matching during recognition. At recognition time, those indexing vectors with the highest match probability are retrieved from the model image database, using a nearest neighbor search algorithm. The nearest neighbor search predicts the best possible match candidates. Extended searches are guided by a search strategy that employs knowledge-base (KB) selection criteria. The knowledge-based system simplifies the recognition process and minimizes the number of iterations and memory usage; Novel contributions include the use of a feature-based indexing data structure together with a knowledge base. Both components improve the efficiency of the recognition process by improved structuring of the database of object features and reducing data base size. This data base organization according to object features facilitates machine learning in the context of a knowledge-base driven recognition algorithm. Lastly, feature-based indexing permits the recognition of 3D objects based on a comparatively small number of stored views, further limiting the size of the feature database; Experiments with real images as well as synthetic images including occluded (partially visible) objects are presented. The experiments show almost perfect recognition with feature-based indexing, if the detected features in the test scene are viewed from the same angle as the view on which the model is based. The experiments also show that the knowledge base is a highly effective and efficient search tool recognition performance is improved without increasing the database size requirements. The experimental results indicate that feature-based indexing in combination with a knowledge-based system will be a useful methodology for automatic target recognition (ATR)

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Process Capability Database Usage In Industry: Myth vs. Reality

Process capability data (PCD) is needed for robust design, optimal tolerance allocation, and variation simulation analysis. Process capability databases (PCDBs) have been developed in many industries and are being used by the manufacturing community to monitor quality; however, they are not being effectively utilized by design. When the PCDBs1 were developed, the intent was for design to use PCD for optimization and product cost minimization, but this ideal situation has not been realized. A survey of a variety of design and manufacturing companies was circulated to determine both the state-ofthe- art in PCDBs and the barriers preventing design from fully utilizing PCD. Two key barriers were identified for internal PCDBs: lack of a company-wide vision for PCD usage and poor communication between manufacturing and design. Supplier PCDBs have the additional barriers of lack of trust between suppliers and customers and time lag for data entry. Management support, training, database population, and common systems were identified as potential solutions to the identified barriers

UNLV Transmutation Research Program Proposal Year III: Design and Evaluation of Processes for Fuel Fabrication

The objective of this project is the design and evaluation of manufacturing processes for transmuter fuel fabrication. The large-scale deployment of remote fabrication and refabrication processes will be required for all transmutation scenarios. Current program emphasis is on a five-year effort to determine the feasibility of transmutation as a technology to limit the need for repository storage of spent commercial fuel. The evaluation of the fabrication processes will create a decision support data base to document design, operations, and costs. Fabrication processes required for different fuel types differ in terms of equipment types, throughput, and cost. Differential cost Implications of various fuel choices will be assessed. The ongoing year 1 project has been focusing on collecting information on existing technologies, equipment costs, and material throughput. Another aspect during years 1 and 2 has been the assessment of robotic technology and robot supervision and control, and the simulation of material handling operations using 3-D simulation tools with view towards the development of a fully automated and reliable, autonomous manufacturing process. Such development has the potential to decrease the cost of remote fuel fabrication and to make transmutation a more economically viable process. An added benefit would be the potential for exposure dose reductions to workers. This project is being conducted in close cooperation with the fabrication development group at Argonne National Lab. Year 3 of the project will be devoted to developing further data and knowledge regarding the cost and feasibility of automated fuel manufacture in a hot cell. The detailed simulation of manufacturing processes (as robotic operations supervised by remote operators and as virtual mock-up facilities) will be continued. Both normal operations as well as failure scenarios will be investigated, analyzed, and simulated. The results of this study will be documented in detail. The results of the simulations will be used by Advanced Fuel Cycle Initiative (AFCI) program personnel to perform sensitivity studies on the impact of different fuel types on AFCI system operation. Conceptual designs for plant designs and the accompanying supervision and control systems will be developed. Impacts on transmutation system capital cost, economics of operation, estimates of process loss, and environmental and safety issues will be estimated in further detail, continuing the work from year 2